Bi-Dimensional Approach Based on Graph Neural Network for Alcoholism Predisposition Detection via EEG signals

Detecting alcoholism is challenging because unreliable patient information impairs early diagnosis and treatment. However, EEG exams offer more reliable data. This paper introduces a new two-dimensional approach for the automatic diagnosis of alcoholism using EEG signals. The approach analyzes changes in neural activity and highlights the impact of high and low-frequency signals by representing each patient as a node in a graph. It combines two-dimensional feature extraction from classical approaches to recent state-of-the-art Computer Vision techniques, such as Transfer Learning with Convolutional Neural Networks(CNN). The methodology to evaluate our proposal used 22 combinations of the traditional feature extraction methods and 34 combinations of recent CNN architectures used as feature extractors combined with Graph Neural Network and Graph Convolutional Network (GCN). The best results were achieved by combining GCN and GLCM-6 in Accuracy (99.35%), F1-score (99.22%), and Recall (98.62%), outperforming state-of-the-art methods by 2%. This approach can support computer-aided diagnoses and early clinical detection of the disease.